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9,302 results for “DETECT”
Additional files for Horvath et al., 2024. Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning.
<p>Additional data for Horvath et al., 2024 (source code freeze, models, data, supplementary figures, tables and files(.</p>
CO emission line spectra of the detected IRAM 30m CO-CAVITY galaxies.
<p>Figures show the observed spectra of the CO(1-0) and CO(2-1) emission lines of the CO-CAVITY galaxies.</p>
Catalogues of LISA-detectable sBHB inspirals and confusion backgrounds following the GWTC-3 fiducial model posterior
<h2>Data set</h2> <p>The contents of <code>SNR_min_2_z1_LISA_SNR.tar.gz</code> consist of approximately 10k folders, each corresponding to each of the samples of the public GWTC-3 fiducial sBHB population model posterior, and containing the following files:</p> <ul> <li><code>population.yaml</code>: population parameters of this sample.</li> <li><code>background.txt</code>: frequencies and characteristic strain squared of the sBHB confusion noise in the LISA band for this population.</li> <li><code>population_detector_frame_SNR.h5</code>: subset of loud sBHB sources, including but not limited to those with LISA SNR larger than 4 (missing in some samples). </li> </ul> <p>For a description of the population parameters in <code>population.yaml</code> and the individual source parameters in <code>population_detector_frame_SNR.h5</code>, see the contents of the <code>Demo.ipynb</code> notebook.</p> <p>To be able to run the notebooks described below, uncompress the <code>SNR_min_2_z1_LISA_SNR.tar.gz</code> file inside a <code>data/</code> subfolder under that of the notebook.</p> <h2>Demo notebook</h2> <p>For examples of how to load and process the catalogues, see the <code>Demo.ipynb</code> notebook.</p> <p>This notebook requires the following Python packages:</p> <p> <code>numpy, scipy, pandas, matplotlib, pyyaml, tqdm</code><br> <br>Some of the plots in the notebook require the <a href="https://github.com/JesusTorrado/extrapops" target="_blank" rel="noopener">extrapops</a> simulation package:</p> <p> <code>$ git clone git@github.com:JesusTorrado/extrapops.git</code><br> <code>$ cd extrapops</code><br> <code>$ pip install .</code></p> <h2>References</h2> <p>For detailed descriptions of the generation of the data set, see the references mentioned in the Zenodo page.</p> <h2>Questions and comments</h2> <p>Please use the <a href="https://github.com/JesusTorrado/LISA_sBHB_catalogues/issues">GitHub issue tracker</a> or contact the corresponding authors of the papers cited under the "described by" header of the Zenodo entry.</p>
CLDF dataset underlying the study "First steps towards the detection of contact layers in Bangime: a multi-disciplinary, computer-assisted approach" from 2022
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hantgan, Abbie and Babiker, Hiba and List, Johann-Mattis (2022): First steps towards the detection of contact layers in Bangime: a multi-disciplinary, computer-assisted approach [version 2; peer review: 2 approved]. Open Research Europe 2022, 2:10.</p> </blockquote>
EagleEye: A general purpose density anomaly detection method
<p>The Air2m_northern_DJF.npy and Air2m_northern_JJA.npy datasets is derived from the <em>NCEP-NCAR Reanalysis 1 data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at <a href="https://psl.noaa.gov/">https://psl.noaa.gov</a></em>., a robust atmospheric dataset that includes a wide range of climatic measurements essential for comprehensive climate analysis. The original data can be accessed at the NOAA Physical Sciences Laboratory website: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html.</p> <p>The LHC Olympics R&D dataset used in the article can be downloaded from https://zenodo.org/records/4536377 .</p> <pre> </pre> <p> </p>
Enhanced Vehicle Detection via YOLOv7-Tiny, dataset we used
<p>Our improvement on YOLOv7-tiny, with Sim_DFC, Inner-shape IoU, and BiFPN. These are the datasets we used.</p>
SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection
<h2>Abstract </h2> <blockquote> <p><em>Developing robust drone detection systems is often constrained by the limited availability of large-scale annotated training data and the high costs associated with real-world data collection. However, synthetic data presents a promising and cost-effective solution to overcome this issue. Therefore, we present SynDroneVision, a synthetic dataset specifically designed for RGB-based drone detection in surveillance applications. Featuring diverse backgrounds, lighting conditions, and drone models, SynDroneVision offers a comprehensive training foundation for deep learning algorithms. To evaluate the dataset's effectiveness, we perform a comparative analysis across a selection of recent YOLO detection models. Our findings demonstrated that SynDroneVision is a valuable resource for real-world data enrichment, achieving notable enhancements in model performance and robustness, while significantly reducing the time and costs of real-world data acquisition. </em> </p> </blockquote> <h2><strong>Paper</strong></h2> <h3><strong>Published in the Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV2025)!</strong></h3> <p>SynDroneVision is presented in the paper <strong>SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection</strong> by Tamara R. Lenhard, Andreas Weinmann, Kai Franke, and Tobias Koch. This work is published in the Proceedings of the <strong>2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV2025)</strong>.</p> <p>The preprint is currently available on ArXiv: <a href="https://arxiv.org/abs/2411.05633v1">here</a></p> <p>The final version is now published in the proceedings of WACV 2025: <a href="https://ieeexplore.ieee.org/document/10943801" target="_blank" rel="noopener">here</a> </p> <h2><strong>Dataset Details</strong></h2> <p>SynDroneVision comprises a total of<strong> 140,038</strong> <strong>annotaed RGB images</strong> (131,238 for training, 8,800 for validation, and 4,000 for testing), featuring a resolution of <strong>2560x1489</strong> pixels. All images are recorded in a sequential manner using <a href="https://www.unrealengine.com/en-US/">Unreal Engine 5.0</a> in combination with <a href="https://github.com/CodexLabsLLC/Colosseum">Colosseum</a>. Apart from drone images, SynDroneVision also includes ~7% of background images (i.e., imag frames without drone instances).</p> <p><strong>Annotation Format:</strong> Annotations (bounding boxes) are provided via text files according to the <strong>YOLO standard format</strong></p> <pre><code><object-class> <x> <y> <width> <height></code></pre> <p>Here, <code><x></code> and <code><y></code> represent the normalized coordinates of the bounding box center, while <code><width></code> and <code><height></code> denote the normalized bounding box wisth and height. In SynDroneVision, <code><object-class></code> is always 0, indicating the drone class.</p> <h2><strong>Download</strong></h2> <p>The SynDroneVision dataset offers around 900 GB of data dedicated to image-based drone detection. To facilitate the download process, we have partitioned the dataset into smaller sections. Specifically, we have divided the training data into 10 segments, organized by sequences.</p> <p>Annotations are available below, with image data accessible via the following links:</p> <table> <tbody> <tr> <td><strong>Dataset Split<br></strong></td> <td><strong>Sequences<br></strong></td> <td><strong>File Name<br></strong></td> <td><strong>Link</strong></td> <td><strong>Size (GB)<br></strong></td> </tr> <tr> <td>Training Set</td> <td>Seq. 001 - 009</td> <td>images_train_seq001-009.zip</td> <td><a href="https://datastore.dlr-pi.de/s/ted96QK36RmXgLs" target="_blank" rel="noopener">Training images PART 1</a></td> <td>57</td> </tr> <tr> <td> </td> <td>Seq. 010 - 018</td> <td>images_train_seq010-018.zip</td> <td><a href="https://datastore.dlr-pi.de/s/28GtS3qeNGksWk7" target="_blank" rel="noopener">Trainng images PART 2</a></td> <td>95.4</td> </tr> <tr> <td> </td> <td>Seq. 019 - 027</td> <td>images_train_seq019-027.zip</td> <td><a href="https://datastore.dlr-pi.de/s/DLCwRBRnsf53Rg7" target="_blank" rel="noopener">Training images PART 3</a></td> <td>96.2</td> </tr> <tr> <td> </td> <td>Seq. 028 - 035</td> <td>images_train_seq028-035.zip</td> <td><a href="https://datastore.dlr-pi.de/s/oF9PmFCexHbB2bp" target="_blank" rel="noopener">Training images PART 4</a></td> <td>83.9</td> </tr> <tr> <td> </td> <td>Seq. 036 - 043</td> <td>images_train_seq036-043.zip</td> <td><a href="https://datastore.dlr-pi.de/s/eepMQrixWXNXNYS" target="_blank" rel="noopener">Training images PART 5</a></td> <td>77.1</td> </tr> <tr> <td> </td> <td>Seq. 044 - 050</td> <td>images_train_seq044-050.zip</td> <td><a href="https://datastore.dlr-pi.de/s/FPnyEAcmpjomY9q" target="_blank" rel="noopener">Training images PART 6</a></td> <td>84.7</td> </tr> <tr> <td> </td> <td>Seq. 051 - 056</td> <td>images_train_seq051-056.zip</td> <td><a href="https://datastore.dlr-pi.de/s/awsHAJqKe85AqiG" target="_blank" rel="noopener">Training images PART 7</a></td> <td>86.8</td> </tr> <tr> <td> </td> <td>Seq. 057 - 065</td> <td>images_train_seq057-065.zip</td> <td><a href="https://datastore.dlr-pi.de/s/2LH8TmAjG94r26P" target="_blank" rel="noopener">Training images PART 8</a></td> <td>86.2</td> </tr> <tr> <td> </td> <td>Seq. 066 - 070</td> <td>images_train_seq066-070.zip</td> <td><a href="https://datastore.dlr-pi.de/s/BGkLANPT3mAwEw8" target="_blank" rel="noopener">Training images PART 9</a></td> <td>75.7</td> </tr> <tr> <td> </td> <td>Seq. 071 - 073</td> <td>images_train_seq071-073.zip</td> <td><a href="https://datastore.dlr-pi.de/s/WCEQPCioNxdqJr9" target="_blank" rel="noopener">Training images PART 10</a></td> <td>38.5</td> </tr> <tr> <td>Validation Set</td> <td>Seq. 001 - 073</td> <td>images_val.zip</td> <td><a href="https://datastore.dlr-pi.de/s/9nYjwxGXJe5ws7g" target="_blank" rel="noopener">Validation images</a></td> <td>55.2</td> </tr> <tr> <td>Test Set</td> <td>Seq. 001 - 073</td> <td>images_test.zip</td> <td><a href="https://datastore.dlr-pi.de/s/5YgqR75oBByEz6R" target="_blank" rel="noopener">Test images</a></td> <td>26.5</td> </tr> </tbody> </table> <h2><strong>Citation</strong></h2> <p>If you find SynDroneVision helpful in your research, we kindly ask that you cite the associated paper. Below is the citation in BibTeX format for your convenience:</p> <p><strong>BibTeX:</strong></p> <pre>@INPROCEEDINGS{10943801, author={Lenhard, Tamara R. and Weinmann, Andreas and Franke, Kai and Koch, Tobias}, booktitle={2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, title={SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection}, year={2025}, volume={}, number={}, pages={7637-7647}, doi={10.1109/WACV61041.2025.00742}} <br><br></pre> <p><em>SynDroneVision uses Unreal® Engine. Unreal® is a trademark or registered trademark of Epic Games, Inc. in the United States of America and elsewhere.</em></p>
CVoiceFake-Full ("SafeEar: Content Privacy-Preserving Audio Deepfake Detection", ACM CCS 2024)
<h1><strong>Introduction:</strong></h1> <p>CVoiceFake (Full) encompasses <strong>five common languages (English, Chinese, German, French, and Italian)</strong> and utilizes <strong>multi-advanced and classical voice cloning techniques</strong> (Parallel WaveGAN, Multi-band MelGAN, Style MelGAN, Griffin-Lim, WORLD, and DiffWave) to produce audio samples that bear a high resemblance to authentic audio.</p> <ol> <li><strong>Parallel WaveGAN</strong>: As a non-autoregressive vocoder-based model, Parallel WaveGAN produces high-fidelity audio rapidly, ideal for efficient and quality deepfake generation.</li> <li><strong>Multi-band MelGAN</strong>: Multi-band MelGAN is a variant of MelGAN that divides the frequency spectrum into sub-bands for faster and more stable multi-lingual vocoder training, enhancing the robustness and scalability of the dataset.</li> <li><strong>Style MelGAN</strong>: Style MelGAN is designed to capture fine prosodic and stylistic nuances of speech, making it particularly compelling for deepfake applications that require high levels of expressivity and variation in speech synthesis.</li> <li><strong>Griffin-Lim</strong>: This algorithm reconstructs waveforms from spectrograms using an iterative phase estimation method. Though less high-fidelity than neural vocoders, it serves as a traditional baseline for comparing deepfake generation.</li> <li><strong>WORLD</strong>: WORLD is a statistical parameter-based voice synthesis system that offers fine control over the spectral and prosodic features of the synthesized audio. Its fine manipulation is useful for crafting the nuanced variations needed in deepfake datasets.</li> <li>We have also built the SOTA diffusion-based deepfake audio (DiffWave); please contact the author at <code>xinfengli@zju.edu.cn</code> if you are interested in the dataset, particularly the DiffWave portion. Furthermore, any additional discussions are welcomed.<br><strong>DiffWave</strong>: DiffWave is a diffusion probability model for waveform generation. It converts the white noise signal into structured waveform through a Markov chain, capable of both conditional and unconditional generation tasks. DiffWave represents the advanced synthesis method for its fast synthesis speed and high synthesis quality.</li> </ol> <h1><strong>🔥 News:</strong></h1> <p>Please note that we recently released our DiffWave subset in Version 2 in comparison to Version 1. You can download the file named CVoiceFake_Large_diffwave_update.tar.gz.xx, and after unzipping it, you will find it retains the same file structure as before.<br> <strong>| CVoiceFake_Large_diffwave_update.tar.gz.00 |<br> | CVoiceFake_Large_diffwave_update.tar.gz.01 |</strong></p> <p> </p> <h1><strong>Full Dataset & Project Page:</strong></h1> <p>The sampled small dataset is available on <a href="11124319" target="_blank" rel="noopener">CVoiceFake Small</a> as well. Please kindly also refer to the project page: <a title="SafeEar Website" href="https://safeearweb.github.io/Project/" target="_blank" rel="noopener">SafeEar Website</a>.</p> <p> </p> <h1><strong>Citation:</strong></h1> <p>If you find our paper/code/dataset helpful, please kindly consider citing this work with the following reference:</p> <pre>@inproceedings{li2024safeear,<br> author = {Li, Xinfeng and Li, Kai and Zheng, Yifan and Yan, Chen and Ji, Xiaoyu, and Xu, Wenyuan},<br> title = {{SafeEar: Content Privacy-Preserving Audio Deepfake Detection}},<br> booktitle = {Proceedings of the 2024 {ACM} {SIGSAC} Conference on Computer and Communications Security (CCS)}<br> year = {2024},<br>}</pre>
Sampled sentence pairs from SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
<p>Each dataset consists of samples, containing two sentences with positions of one of the given target words. In every sample, first sentence is taken from corpus1 and second from corpus2. Initial sentences were taken from https://www.ims.uni-stuttgart.de/en/research/resources/corpora/sem-eval-ulscd/. </p>
Dump truck object detection with manual annotations
<p>Doing manual annotations can sometimes be resource heavy, depending on the amount of data. This dataset was designed to created to use in conjunction with a semi-automatic annotation method based on linear interpolation. The dataset contains 799 images, where 679 lies in the trainingset, and the rest lies in the validationset. The images are taken from 6 different video streams, where a remote controlled wheel loader approaches a miniature dump truck at different angles. 4 of the videos are used in the trainingset. The labels can contain up to 5 classes which are:</p> <p>0 - front wheel <br> 1 - middle wheel<br> 2 - back wheel<br> 3 - tipping body<br> 4 - cap</p> <p>This dataset was used to train a YOLOv3 model, hence the labels will be written in the YOLO labeling format.</p>
Data from: Detection of the endangered European weather loach (Misgurnus fossilis) via water and sediment samples: testing multiple eDNA workflows.
<p>The European weather loach (<i>Misgurnus fossilis</i>) is classified as highly endangered in several countries of Central Europe. Populations of <i>M. fossilis</i> are predominantly found in ditches with low water levels and thick sludge layers and are thus hard to detect using conventional fishing methods. Therefore, environmental DNA (eDNA) monitoring appears particularly relevant for this species. In previous studies, <i>M. fossilis</i> was surveyed following eDNA water sampling protocols, which were not optimized for this species. Therefore, we created two full factorial study designs to test six different eDNA workflows for sediment samples and twelve different workflows for water samples. We used qPCR to compare the Threshold cycle (Ct) values of the different workflows, which indicate the target DNA amount in the sample, and spectrophotometry to quantify and compare the total DNA amount inside the samples. We analyzed 96 water samples and 48 sediment samples from a pond with a known population of <i>M. fossilis</i>. We tested several method combinations for long-term sample preservation, DNA capture and DNA extraction. Additionally, we analyzed the DNA yield of samples from a ditch with a natural <i>M. fossilis</i> population monthly over one year to determine the optimal sampling period. Our results showed that the long-term water preservation method commonly used for eDNA surveys of <i>M. fossilis </i>did not lead to optimal DNA yields, and we present a valid long-term sample preservation alternative. A cost-efficient high salt DNA extraction led to the highest target DNA yields and can be used for sediment and water samples. Furthermore, we were able to show that in a natural habitat of <i>M. fossilis</i>, total and target eDNA were higher between June and September, which implies that this period is favorable for eDNA sampling. Our results will help to improve the reliability of future eDNA surveys of <i>M. fossilis</i>.</p>
MRI versus CT in the detection of brain lesions in patients with infective endocarditis before/after cardiac surgery
<p>Imaging of brain involvement in infective endocarditis can drive the clinical management of this serious condition. MRI is very sensitive, but CT is more readily available. In this retrospective study, we compared the detection rate of CT and MRI.</p> <p>After Ethics Committee approval, we retrospectively reviewed a series of 20 patients (13 males, median age 64 years) who underwent both CT and MRI before or after cardiac surgery for definite infective endocarditis. Unenhanced CT and MRI were evaluated for acute ischemic lesions, both punctuate and large, intraparenchymal hemorrhages, cerebral microbleeds, subarachnoid hemorrhages, abscesses, microabscesses and meningitis. Qualitative and Cohen κ analysis were performed. The value of contrast-enhanced scans (MRI, <em>n</em> = 14; CT, <em>n</em> = 9) and cognitive status were also assessed.</p> <p>While CT rapidly excludes large hemorrhages before cardiac surgery for infective endocarditis, MRI accurately distinguishes the whole spectrum of brain lesions.</p>
Replication Package for "Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis"
<p>This repository provides the data sets and scripts used in the paper "Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis" by Sebastian Nielebock, Robert Heumüller, Kevin Michael Schott, and Frank Ortmeier from the Faculty of Computer Science of the Otto-von-Guericke University Magdeburg, Germany. This paper is published in Springer's "Automated Software Engineering - An International Journal" in August 2021. The article is available as open access at <a href="https://dx.doi.org/10.1007/s10515-021-00294-x">https://dx.doi.org/10.1007/s10515-021-00294-x</a>. A preprint is available under <a href="https://arxiv.org/abs/2008.00277">https://arxiv.org/abs/2008.00277</a>.</p> <p>All scripts and data sets are provided by the authors and come without any guarantee. For any issues regarding replication do not hesitate to contact us ({sebastian.nielebock,robert.heumueller, kevin.schott, frank.ortmeier} <at> ovgu.de)</p> <p>If you use or refer to these datasets, please cite our paper using the following BibTex entry.</p> <pre>@article{NielebockAPIFilterSearch2021, title = {Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis}, author = {Sebastian Nielebock and Robert Heum\"{u}ller and Kevin Michael Schott and Frank Ortmeier}, editor = {Springer}, journal = {Springer Automated Software Engineering - An International Journal}, number = {15}, pages = {1-48}, volume = {28}, url = {https://arxiv.org/abs/2008.00277}, doi = {10.1007/s10515-021-00294-x}, year = {2021}, } </pre>
Artifacts for the FDL21 Paper: In-Vivo Stack Overflow Detection and Stack Size Estimation for Low-End Multithreaded Operating Systems using Virtual Prototypes
<p>Artifacts for the evaluation of the paper <em>In-Vivo Stack Overflow Detection and Stack Size Estimation for Low-End Multithreaded Operating Systems using Virtual Prototypes</em> which will be published as part of <a href="http://www.fdl-conference.org">FDL21</a>. The artifacts provided here use pre-compiled binaries and pre-generated stack usage databases. Based on these provided files, the stack size estimation and performance evaluation performed in the paper can be replicated. Various README.md files, which provide more information on individual artifacts, are also included.</p> <p>The software used in conjunction with these artifacts is also freely available on GitHub:</p> <ul> <li>https://github.com/agra-uni-bremen/fdl21-stackuse-vp</li> <li>https://github.com/agra-uni-bremen/stack-usage-db</li> </ul>
Data from: Efficient summary statistics for detecting lineage fusion from phylogeographic datasets
<p>Aim: Lineage fusion (merging of two or more populations of a species resulting in a single panmictic group) is a special case of secondary contact. It has the potential to counteract diversification and speciation, or to facilitate it through creation of novel genotypes. Understanding the prevalence of lineage fusion in nature requires reliable detection of it, such that efficient summary statistics are needed. Here we report on simulations that characterized the initial intensity and subsequent decay of signatures of past fusion for 17 summary statistics applicable to DNA sequence haplotype data.</p> <p>Location: Global.</p> <p>Taxon: Diploid out-crossing species.</p> <p>Methods: We considered a range of scenarios that could reveal the impacts of different combinations of read length versus number of loci (arrangement of DNA sequence data), and whether or not pre-fusion populations experienced bottlenecks coinciding with their divergence (historical context of fusion). Post-fusion gene pools were sampled along 10 successive time points representing increasing lag times following merging of sister populations, and summary statistic values were recalculated at each.</p> <p>Results: Many summary statistics were able to detect signatures of complete merging of populations after a sampling lag time of 1.5 Ne generations, but the most informative ones included two neutrality tests and four diversity metrics, with ZnS being particularly powerful. Correlation was relatively low among the two neutrality tests and two of the diversity metrics. There were clear benefits of many short (200-bp × 200) loci over a handful of long (4-kb × 10) loci. Also, only the latter genetic dataset type was showed impacts of bottlenecks during divergence upon the number of informative summary statistics.<br> Main conclusions: This work contributes to identifying cases of lineage fusion, and advances phylogeography by enabling more nuanced reconstructions of how individual species, or multiple members of an ecological community, responded to past environmental change.</p>
Object Detection meets Knowledge Graphs Raw Data
<p>Raw data used to reproduce the results of 'Object Detection meets Knowledge Graphs'. Combination of VOC 2007 images, MS COCO 2014 images and ConceptNet knowledge graph. Also the checkpoints of the two trained models for the VOC dataset and COCO dataset.</p>
Detection of Neoplasms by Metagenomic Sequencing of Cerebrospinal Fluid
<p>Detection of Neoplasms by Metagenomic Sequencing of Cerebrospinal Fluid</p> <p>Wei Gu, MD, PhD<sup>1,2</sup>*, Andreas M. Rauschecker, MD, PhD<sup>3</sup>, Elaine Hsu, BS<sup>1</sup>, Kelsey C. Zorn, BS<sup>4</sup>, Yasemin Sucu, BS<sup>1</sup>, Scot Federman, BS<sup>1</sup>, Allan Gopez, BS<sup>1</sup>, Shaun Arevalo, BS<sup>1</sup>, Hannah A. Sample, BS<sup>4</sup>, Eric Talevich, PhD<sup>5</sup>, Eric D. Nguyen, MD, PhD<sup> 1</sup>, Marc Gottschall, BS<sup>1</sup>, Bardia Nourbakhsh, MD, MAS<sup>6</sup>, Carl A. Gold, MD, MS<sup>7</sup>, Bruce A.C. Cree, MD, PhD, MAS<sup>8</sup>, Vanja Douglas, MD<sup>8</sup>, Megan B. Richie, MD<sup>8</sup>, Maulik P. Shah, MD, MHS<sup>8</sup>, S. Andrew Josephson, MD<sup>8</sup>, Jeffrey M. Gelfand, MD, MAS<sup>8</sup>, Steve Miller, MD, PhD<sup> 1</sup>, Linlin Wang, MD<sup>1</sup>, Tarik Tihan, MD, PhD<sup>9</sup>, Joseph L. DeRisi, PhD<sup>4,10</sup>, Charles Y. Chiu, MD, PhD<sup>11,12</sup>, Michael R. Wilson, MD, MAS<sup>8</sup>*</p> <p> </p> <p><sup>1</sup>Department of Laboratory Medicine, University of California San Francisco, CA 94107, USA</p> <p><sup>2</sup>Department of Pathology, Stanford University, CA 94305, USA</p> <p><sup>3</sup>Department of Radiology and Biomedical Imaging, University of California San Francisco, CA 94107, USA</p> <p><sup>4</sup>Department of Biochemistry and Biophysics, University of California San Francisco, CA 94107, USA</p> <p><sup>5</sup>DNANexus, Mountain View, CA 94040, USA</p> <p><sup>6</sup>Department of Neurology, Johns Hopkins University, MD 21287, USA</p> <p><sup>7</sup>Department of Neurology, Stanford University, CA 94305, USA</p> <p><sup>8</sup>Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA 94107, USA</p> <p><sup>9</sup>Department of Pathology, University of California San Francisco, CA 94107, USA</p> <p><sup>10</sup>Chan Zuckerberg Biohub, San Francisco, CA 94107, USA</p> <p><sup>11</sup>UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA 91407, USA</p> <p><sup>12</sup>Department of Medicine, Division of Infectious Diseases, University of California San Francisco, CA 94107, USA</p> <p> </p> <p>* Corresponding authors:</p> <p>Michael Wilson, MD, MAS</p> <p>UCSF Department of Neurology</p> <p>675 Nelson Rising Lane, NS212</p> <p>San Francisco, CA 94158</p> <p><a href="mailto:Michael.Wilson@ucsf.edu">Michael.Wilson@ucsf.edu</a></p> <p> </p> <p>Wei Gu, MD, PhD</p> <p>Stanford University, Department of Pathology</p> <p>3373 Hillview Ave, 218</p> <p>Palo Alto, CA 94304</p> <p>650-723-1914</p> <p><a href="mailto:weigu@stanford.edu">weigu@stanford.edu</a></p>
The crater detection results (necessary data for making Figs 6 and 7) for areas A2, A4 and A7 to estimate the ages of RMDS-overlapped craters in Figs 2a-2c
<p>To estimate the ages of RMDS-overlapped craters in Figs 2a-2c ( located in areas A2, A4 and A7, respectively) with a locally calibrated Monte Carlo model, the NAC-DEM-based crater detection results are provided here, as the basis input and necessary data to make Figs 6 and 7.</p> <p>They have been introduced in the following publication, where more details can be found:</p> <p>F. Zhang, J. W. Head, C. Wöhler, A. T. Basilevsky, L. Wilson, M. Xie, R. Bugiolacchi, T. Wilhelm, S. Althoff, Y. L. Zou.</p> <p>The Lunar Mare Ring-Moat Dome Structure (RMDS) Age Conundrum: Contemporaneous with Imbrian-Aged Host Lava Flows or Emplaced in the Copernican?</p> <p>Journal of Geophysical Research: Planets, 126, e2021JE006880. https://doi. org/10.1029/2021JE006880</p>
Supplementary dataset for "Detection of "rapid" aseismic slip at the Izu-Bonin Trench"
<p>This dataset is the supplementary dataset for the research article "Detection of "rapid" aseismic slip at the Izu-Bonin Trench" by Fukao Y., et al. (2021, 10.1029/2021JB022132).</p> <p>The zip-archived file SupplementaryMaterial.zip contains the observation data of the pressure array experiment used in this study.<br> The data format is summarized in README.pdf. Time stamps are given in UT.</p> <p>The seafloor pressure observation experiment was made under the support of JSPS Kakenhi 25247074, 17K05646, 20K04142. The observations were made by the research cruises YK15-08 (R/V Yokosuka, JAMSTEC) and MR16-E02 (R/V Mirai, JAMSTEC).</p> <p> </p> <p> </p> <p> </p> <div> </div>
Investigating automated bird detection from webcams using machine learning
<p>We provide a dataset of images(.jpeg) with their corresponding annotations files(.xml) used to train a bird detection deep learning model. These images were collected from the live stream feeds of Cornell Lab of Ornithology (https://www.allaboutbirds.org/cams/) situated in 6 unique locations around the world as follows:</p> <ul> <li>Treman bird feeding garden at the Cornell Ornithology Laboratory in Ithaca, New York. At this station, Axis P11448-LE cameras are used to capture the recordings from feeders perched on the edge of both Sapsucker Woods and its 10-acre ponds. This site mainly attracts forest species like chickadees (Poecile atricapillus), red-winged blackbirds (Agelaius phoeniceus), and woodpeckers (Picidae). A total of 2065 images were captured from this location.</li> <li> Fort Davis in Western Texas, USA. At this site, a total of 30 hummingbird feeder cams are hosted at an elevation of over 5500 feet. From this site, 1440 images were captured.</li> <li>Sachatamia Lodge in Mindo, Ecuador. This site has a live hummingbird feed watcher that attracts over 132 species of hummingbirds including: Fawn-breasted Brilliant, White-necked Jacobin, Purple-bibbed Whitetip, Violet-tailed Sylph, Velvet-purple Coronet, and many others. A total of 2063 images were captured from this location.</li> <li>Morris County, New Jersey, USA. Feeders at this location attract over 39 species including Red-bellied Woodpecker, Red-winged Blackbird, Purple Finch, Blue Jay, Pine Siskin, Hairy Woodpecker, and others. Footage at this site is captured by an Axis P1448-LE Camera and Axis T8351 Microphone. A total of 1876 images were recorded from this site.</li> <li>Canopy Lodge in El Valle de Anton, Panama. Over 158 bird species visit this location annually and these include Gray-headed Chachalaca, Ruddy Ground-Dove, White-tipped Dove, Green Hermit, and others. A total of 1600 images were captured.</li> <li>Southeast tip of South Island, New Zealand. At this site, nearly 10000 seabirds visit this location annually and a total of 1548 images were captured.</li> </ul> <p> The Cornell Lab of Ornithology is an institute dedicated to biodiversity conversation with the main focus on birds through research, citizen science, and education. The autoscreen software was used to capture the images from the live feeds and images of approximately 1 Megapixel (Joint Photographic Experts Group) JPEG-coloured images of resolution 1366 X 768 X 3 pixels were collected (https://sourceforge.net/projects/autoscreen/). The software took a new image every 30 seconds and was captured during different times of the day in order to avoid a sample-biased dataset. In total, 10592 images were collected for this study.</p> <p><strong>Files provided</strong></p> <p>Train.zip – contains 6779 image files(.jpeg) and 6779 annotation files (.xml)</p> <p>Validation.zip – contains 1695 image files(.jpeg) and 1695 annotation files (.xml)</p> <p>Test.zip –contains 2118 image files(.jpeg)</p> <p>Scripts.zip - Contains scripts needed in manipulating the dataset like dataset partitioning, and creation of CSV and tfrecords files. </p> <p>This dataset was used in the MSc thesis titled “Investigating automated bird detection from webcams using machine learning” by Alex Mirugwe, University of Cape Town – South Africa.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.